ETHFDUSD

CompletedLoses to ETHFDUSD B&H· α -18.30%

LowMoney4/27/2026, 11:07:37 AM

ETHFDUSD | 5LowMoney.json | 2025-01-01 - 2025-12-31 | -29.86% | 5992 trades | 100% WR

Final Value
1176.22 USDT
Return
-29.86%
Profit
+186.08 USDT
Trades
5992
Win Rate
100.0%
Open Orders
68
Best Trade
+0.038204 USDT
Worst Trade
+0.024547 USDT
Max Drawdown
-58.94%
Profit Factor
Sharpe
-0.14
Wins / Losses
5992 / 0
TP / SL / TSL
5992 / 0 / 0
Total Fees
226.73 USDT
Max Streak W/L
5992 / 0
Hold P50 / P95
29m / 4.6d

ETHFDUSD Backtest – unCoded Crypto TradingBot

Strategy: LowMoney | Period: 2025-01-01 to 2025-12-31 | Starting Capital: 1,677.00 USDT | Final Value: 1,176.22 USDT | Return: -29.86% | Trades: 5,992 | Win Rate: 100.0% | Best Trade: 0.0382 USDT | Worst Trade: 0.0245 USDT | Total Profit: 186.08 USDT | Max Drawdown: -58.94% | Sharpe Ratio: -0.14 | Total Fees: 226.73 USDT

Strategy Configuration – LowMoney
Buy Trigger: -0.1%
Buy Splits: 2
Investment/Buy: 50 USDT
Start Balance: 1,677.00 USDT
Can Buy Up: Yes
Can Buy Down: No
Stop Loss: No
Maker Fee: 7.5 bps
Taker Fee: 7.5 bps
Sell Zones (2):
+0.25% → 50%+0.3% → 50%

Performance Analysis

A -29.86% return is a clear loss for ETHFDUSD. The LowMoney configuration was a poor fit for ETHFDUSD during this window. Useful as a negative datapoint: it tells you which parameter combinations to avoid for similar ETHFDUSD market regimes.

About ETHFDUSD: Ethereum sits one tier below Bitcoin in market cap and slightly above it in realised volatility. ETH pairs typically reward strategies that can hold through brief drawdowns to capture larger trend moves.

An 100.0% win rate across 5,992 trades on ETHFDUSD is unusually high. Strategies that win this often typically use small take-profits relative to stop-losses, which works until a single large adverse ETHFDUSD move erases many small wins.

At roughly 16.5 ETHFDUSD trades per day this is a high-frequency configuration -- fee drag and slippage assumptions become critical when extrapolating to live trading on Binance Spot.

The trade payoff distribution is positively skewed -- outsized winners drove the bulk of the result, which is characteristic of trend-capturing modes. Best single trade: 0.0382 USDT. Worst: 0.0245 USDT. Average per trade: 0.0311 USDT.

Risk profile: Per-trade exposure was minimal -- the worst trade only cost 0.00% of starting capital. That low-risk-per-trade footprint is the signature of a tightly-sized configuration; expect smoother equity curves but also slower compounding in strong trend regimes. Best single trade contributed +0.00% to the account, giving a single-trade reward-to-risk ratio of roughly 1.56:1 between the extreme outliers.

About the LowMoney strategy: LowMoney is calibrated for small starting balances -- smaller position sizes, tighter risk controls, fewer parallel orders.

Configuration analysis: The LowMoney configuration entered on a 0.1% pullback signal across 2 potential buy splits at 50 USDT each. Total deployable notional is therefore 100 USDT -- a position-sizing footprint that is defensive at 6% of starting capital -- most of the account stays in stablecoins as buffer. No hard stop-loss is configured -- the strategy relies on take-profit zones and trailing logic instead, which trades smoother behaviour for higher tail-risk in sustained downtrends. Profit is taken in 2 laddered sell zones, which scales out gradually rather than betting on a single exit price -- a structure that smooths returns at the cost of capping the very best winners. Maker/taker fees totalling 15 bps were deducted from every fill, so the headline -29.86% is already net of trading costs -- no additional fee adjustment is required when comparing to other runs.

Over the 364-day test window the strategy generated 186.08 USDT of profit on a 1677 USDT starting balance, growing the account to 1176.22 USDT. Annualised, the -29.86% return over 364 days projects to roughly -29.9% per year -- a pace that would erode capital over time if extrapolated. Crypto market regimes shift quickly, so this projection should be treated as a directional indicator rather than a forecast.

Methodology & data

This backtest was executed on historical Binance Spot 1-minute candles for ETHFDUSD, with intrabar fill simulation in "OLHC" mode and a synthetic order latency of 2s applied to each fill to approximate real-world routing delay. The simulator processes every minute sequentially, evaluates the LowMoney rule set, and books fills against the next available bar -- a standard event-driven backtesting approach that avoids look-ahead bias. Equity is marked-to-market on every closed trade and aggregated into the equity curve shown above.

Test window covers approximately 12.0 months of ETHFDUSD 1-minute price action -- a sample size that is large enough to span multiple short-term regimes.

Live trading considerations

Translating this result to live trading: ETHFDUSD liquidity should be checked separately -- fill assumptions can drift if the order book is thin during volatile windows. The high trade frequency means cumulative slippage and exchange-side latency will erode a few percent of the headline return over a full year -- budget for that gap. Without a hard stop-loss, the live system depends on the take-profit ladder firing during recovery legs; a prolonged downtrend without recovery will hold positions open longer than backtest aggregates suggest. Additionally, exchange downtime, API rate limits, and funding-rate changes (on perp variants) are not modelled here and should be accounted for in production deployment.

Frequently asked questions

Is a -29.86% return on ETHFDUSD a good backtest result?
No -- a negative return means the strategy lost capital and the configuration was unsuitable for this market regime.
What does the 100.0% win rate mean here?
It means 100.0 out of every 100 closed trades ended profitable. Frequent wins are emotionally easier to operate but say nothing about size -- one large loss can offset many small wins.
What is the annualised return for this ETHFDUSD backtest?
Compounding the -29.86% over 364 days projects to -29.9% per year. This is a directional indicator only -- crypto regimes change, and strategies rarely sustain peak performance year-over-year.
Can I run this exact LowMoney configuration live?
The configuration shown in the Strategy Configuration block is the same JSON the live unCoded TradingBot consumes, so it is directly deployable. Before going live, validate the run on a paper-trading window, confirm exchange-side fees match the simulated 7.5/7.5 bps, and start with a position size below the backtested capital to absorb live slippage.
How is this backtest different from others on ETHFDUSD?
Every run on the platform uses the same intrabar-fill engine and historical Binance Spot data, so the comparison is apples-to-apples. What differs between runs is the LowMoney parameter set (buy trigger, sell zones, splits, stop-loss) and the time window -- both are visible above so you can rerun, tune, or fork this configuration.

This interpretation is generated deterministically from this run's own metrics. Past performance is not indicative of future results -- a profitable backtest is necessary but not sufficient evidence that a strategy will work in live trading on ETHFDUSD.

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Trades

0 Trades

0 abgeschlossene Trades – unCoded Crypto TradingBot Backtest
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